




import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints

import yaml


snake_case_  :      Optional[int]		=		NewType("DataClass", Any)
snake_case_  :      Tuple		=		NewType("DataClassType", Any)



def        __a							(  __UpperCAmelCase		:						Optional[int]      ) ->					List[Any]:
							"""simple docstring"""




							if isinstance(__UpperCAmelCase   ,		__UpperCAmelCase      ):
														return v
							if v.lower() in ("yes", "true", "t", "y", "1"):
														return True
							elif v.lower() in ("no", "false", "f", "n", "0"):
														return False
							else:
														raise ArgumentTypeError(
														    f"Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive)."      )



def        __a							(  __UpperCAmelCase		:						list      ) ->					Optional[int]:
							"""simple docstring"""




							lowerCamelCase_			:    List[str]   						=      {str(__UpperCAmelCase      ): choice for choice in choices}
							return lambda __UpperCAmelCase      : str_to_choice.get(__UpperCAmelCase   ,		__UpperCAmelCase      )



def        __a							(  *,
    __UpperCAmelCase		:						Union[str, List[str]] = None   ,		__UpperCAmelCase		:						str = None   ,		__UpperCAmelCase		:						Any = dataclasses.MISSING   ,		__UpperCAmelCase		:						Callable[[], Any] = dataclasses.MISSING   ,		__UpperCAmelCase		:						dict = None   ,		**__UpperCAmelCase		:						str   ,		) ->					List[str]:
							"""simple docstring"""




							if metadata is None:
														# Important, don't use as default param in function signature because dict is mutable and shared across function calls
														lowerCamelCase_			:    Dict   						=      {}
							if aliases is not None:
														lowerCamelCase_			:    List[Any]   						=      aliases
							if help is not None:
														lowerCamelCase_			:    Union[str, Any]   						=      help

							return dataclasses.field(metadata=__UpperCAmelCase   ,		default=__UpperCAmelCase   ,		default_factory=__UpperCAmelCase   ,		**__UpperCAmelCase      )





class 					snake_case_   (     snake_case_	):



				'''simple docstring'''
				lowerCamelCase       =  42





				def __init__(      self							:   Union[str, Any]      ,							__magic_name__							:   Union[DataClassType, Iterable[DataClassType]]      ,							**__magic_name__							:   Optional[int]							)							->			Union[str, Any]:
											# To make the default appear when using --help
											if "formatter_class" not in kwargs:
																		lowerCamelCase_			:    int   						=      ArgumentDefaultsHelpFormatter
											super().__init__(**lowerCAmelCase__							)
											if dataclasses.is_dataclass(lowerCAmelCase__							):
																		lowerCamelCase_			:    Union[str, Any]   						=      [dataclass_types]
											lowerCamelCase_			:    Optional[int]   						=      list(lowerCAmelCase__							)
											for dtype in self.dataclass_types:
																		self._add_dataclass_arguments(lowerCAmelCase__							)





				@staticmethod
				def    __SCREAMING_SNAKE_CASE     (      __magic_name__							:   ArgumentParser      ,							__magic_name__							:   dataclasses.Field							)							->			Any:
											lowerCamelCase_			:    List[str]   						=      F"--{field.name}"
											lowerCamelCase_			:    Tuple   						=      field.metadata.copy()
											# field.metadata is not used at all by Data Classes,
											# it is provided as a third-party extension mechanism.
											if isinstance(field.type      ,							lowerCAmelCase__							):
																		raise RuntimeError(
																		    "Unresolved type detected, which should have been done with the help of "
																		    "`typing.get_type_hints` method by default"							)

											lowerCamelCase_			:    Optional[int]   						=      kwargs.pop("aliases"      ,							[]							)
											if isinstance(lowerCAmelCase__      ,							lowerCAmelCase__							):
																		lowerCamelCase_			:    List[str]   						=      [aliases]

											lowerCamelCase_			:    List[Any]   						=      getattr(field.type      ,							"__origin__"      ,							field.type							)
											if origin_type is Union or (hasattr(lowerCAmelCase__      ,							"UnionType"							) and isinstance(lowerCAmelCase__      ,							types.UnionType							)):
																		if str not in field.type.__args__ and (
																		    len(field.type.__args__							) != 2 or type(lowerCAmelCase__							) not in field.type.__args__
																		):
																									raise ValueError(
																									    "Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because"
																									    " the argument parser only supports one type per argument."
																									    F" Problem encountered in field '{field.name}'."							)
																		if type(lowerCAmelCase__							) not in field.type.__args__:
																									# filter `str` in Union
																									lowerCamelCase_			:    Tuple   						=      field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
																									lowerCamelCase_			:    Optional[Any]   						=      getattr(field.type      ,							"__origin__"      ,							field.type							)
																		elif bool not in field.type.__args__:
																									# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
																									lowerCamelCase_			:    str   						=      (
																									    field.type.__args__[0] if isinstance(lowerCAmelCase__      ,							field.type.__args__[1]							) else field.type.__args__[1]
																									)
																									lowerCamelCase_			:    Any   						=      getattr(field.type      ,							"__origin__"      ,							field.type							)

        # A variable to store kwargs for a boolean field, if needed
        # so that we can init a `no_*` complement argument (see below)
											lowerCamelCase_			:    Tuple   						=      {}
											if origin_type is Literal or (isinstance(field.type      ,							lowerCAmelCase__							) and issubclass(field.type      ,							lowerCAmelCase__							)):
																		if origin_type is Literal:
																									lowerCamelCase_			:    Union[str, Any]   						=      field.type.__args__
																		else:
																									lowerCamelCase_			:    Union[str, Any]   						=      [x.value for x in field.type]

																		lowerCamelCase_			:    List[Any]   						=      make_choice_type_function(kwargs["choices"]							)

																		if field.default is not dataclasses.MISSING:
																									lowerCamelCase_			:    Union[str, Any]   						=      field.default
																		else:
																									lowerCamelCase_			:    Tuple   						=      True
											elif field.type is bool or field.type == Optional[bool]:
																		# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
																		# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
																		lowerCamelCase_			:    Tuple   						=      copy(lowerCAmelCase__							)

																		# Hack because type=bool in argparse does not behave as we want.
																		lowerCamelCase_			:    Dict   						=      string_to_bool
																		if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
																									# Default value is False if we have no default when of type bool.
																									lowerCamelCase_			:    List[str]   						=      False if field.default is dataclasses.MISSING else field.default
																									# This is the value that will get picked if we don't include --field_name in any way
																									lowerCamelCase_			:    Union[str, Any]   						=      default
																									# This tells argparse we accept 0 or 1 value after --field_name
																									lowerCamelCase_			:    Dict   						=      '''?'''
																									# This is the value that will get picked if we do --field_name (without value)
																									lowerCamelCase_			:    List[str]   						=      True
											elif isclass(lowerCAmelCase__							) and issubclass(lowerCAmelCase__      ,							lowerCAmelCase__							):
																		lowerCamelCase_			:    Union[str, Any]   						=      field.type.__args__[0]
																		lowerCamelCase_			:    Any   						=      '''+'''
																		if field.default_factory is not dataclasses.MISSING:
																									lowerCamelCase_			:    Dict   						=      field.default_factory()
																		elif field.default is dataclasses.MISSING:
																									lowerCamelCase_			:    Dict   						=      True
											else:
																		lowerCamelCase_			:    List[Any]   						=      field.type
																		if field.default is not dataclasses.MISSING:
																									lowerCamelCase_			:    List[Any]   						=      field.default
																		elif field.default_factory is not dataclasses.MISSING:
																									lowerCamelCase_			:    str   						=      field.default_factory()
																		else:
																									lowerCamelCase_			:    Optional[int]   						=      True
											parser.add_argument(lowerCAmelCase__      ,							*lowerCAmelCase__      ,							**lowerCAmelCase__							)

											# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
											# Order is important for arguments with the same destination!
											# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
											# here and we do not need those changes/additional keys.
											if field.default is True and (field.type is bool or field.type == Optional[bool]):
																		lowerCamelCase_			:    int   						=      False
																		parser.add_argument(F"--no_{field.name}"      ,							action="store_false"      ,							dest=field.name      ,							**lowerCAmelCase__							)





				def    __SCREAMING_SNAKE_CASE     (      self							:   Union[str, Any]      ,							__magic_name__							:   DataClassType							)							->			Tuple:
											if hasattr(lowerCAmelCase__      ,							"_argument_group_name"							):
																		lowerCamelCase_			:    str   						=      self.add_argument_group(dtype._argument_group_name							)
											else:
																		lowerCamelCase_			:    Any   						=      self

											try:
																		lowerCamelCase_			:    Dict[str, type]   						=      get_type_hints(lowerCAmelCase__							)
											except NameError:
																		raise RuntimeError(
																		    F"Type resolution failed for {dtype}. Try declaring the class in global scope or "
																		    "removing line of `from __future__ import annotations` which opts in Postponed "
																		    "Evaluation of Annotations (PEP 563)"							)
											except TypeError as ex:
																		# Remove this block when we drop Python 3.9 support
																		if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(lowerCAmelCase__							):
																									lowerCamelCase_			:    Dict   						=      '''.'''.join(map(lowerCAmelCase__      ,							sys.version_info[:3]							)							)
																									raise RuntimeError(
																									    F"Type resolution failed for {dtype} on Python {python_version}. Try removing "
																									    "line of `from __future__ import annotations` which opts in union types as "
																									    "`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To "
																									    "support Python versions that lower than 3.10, you need to use "
																									    "`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of "
																									    "`X | None`."							) from ex
																		raise

											for field in dataclasses.fields(lowerCAmelCase__							):
																		if not field.init:
																									continue
																		lowerCamelCase_			:    Union[str, Any]   						=      type_hints[field.name]
																		self._parse_dataclass_field(lowerCAmelCase__      ,							lowerCAmelCase__							)





				def    __SCREAMING_SNAKE_CASE     (      self							:   Optional[int]      ,							__magic_name__							:   str=None      ,							__magic_name__							:   List[str]=False      ,							__magic_name__							:   Any=True      ,							__magic_name__							:   List[Any]=None      ,							__magic_name__							:   Dict=None      ,							)							->			str:

											if args_file_flag or args_filename or (look_for_args_file and len(sys.argv							)):
																		lowerCamelCase_			:    List[str]   						=      []

																		if args_filename:
																									args_files.append(Path(lowerCAmelCase__							)							)
																		elif look_for_args_file and len(sys.argv							):
																									args_files.append(Path(sys.argv[0]							).with_suffix(".args"							)							)

																		# args files specified via command line flag should overwrite default args files so we add them last
																		if args_file_flag:
																									# Create special parser just to extract the args_file_flag values
																									lowerCamelCase_			:    Optional[Any]   						=      ArgumentParser()
																									args_file_parser.add_argument(lowerCAmelCase__      ,							type=lowerCAmelCase__      ,							action="append"							)

																									# Use only remaining args for further parsing (remove the args_file_flag)
																									lowerCamelCase_			:    Union[str, Any]   						=      args_file_parser.parse_known_args(args=lowerCAmelCase__							)
																									lowerCamelCase_			:    Tuple   						=      vars(lowerCAmelCase__							).get(args_file_flag.lstrip("-"							)      ,							lowerCAmelCase__							)

																									if cmd_args_file_paths:
																																args_files.extend([Path(lowerCAmelCase__							) for p in cmd_args_file_paths]							)

																		lowerCamelCase_			:    int   						=      []
																		for args_file in args_files:
																									if args_file.exists():
																																file_args += args_file.read_text().split()

            # in case of duplicate arguments the last one has precedence
            # args specified via the command line should overwrite args from files, so we add them last
																		lowerCamelCase_			:    int   						=      file_args + args if args is not None else file_args + sys.argv[1:]
											lowerCamelCase_			:    Union[str, Any]   						=      self.parse_known_args(args=lowerCAmelCase__							)
											lowerCamelCase_			:    Union[str, Any]   						=      []
											for dtype in self.dataclass_types:
																		lowerCamelCase_			:    Any   						=      {f.name for f in dataclasses.fields(lowerCAmelCase__							) if f.init}
																		lowerCamelCase_			:    List[Any]   						=      {k: v for k, v in vars(lowerCAmelCase__							).items() if k in keys}
																		for k in keys:
																									delattr(lowerCAmelCase__      ,							lowerCAmelCase__							)
																		lowerCamelCase_			:    int   						=      dtype(**lowerCAmelCase__							)
																		outputs.append(lowerCAmelCase__							)
											if len(namespace.__dict__							) > 0:
																		# additional namespace.
																		outputs.append(lowerCAmelCase__							)
											if return_remaining_strings:
																		return (*outputs, remaining_args)
											else:
																		if remaining_args:
																									raise ValueError(F"Some specified arguments are not used by the HfArgumentParser: {remaining_args}"							)

																		return (*outputs,)





				def    __SCREAMING_SNAKE_CASE     (      self							:   Optional[int]      ,							__magic_name__							:   Dict[str, Any]      ,							__magic_name__							:   bool = False							)							->			Optional[int]:
											lowerCamelCase_			:    List[Any]   						=      set(args.keys()							)
											lowerCamelCase_			:    int   						=      []
											for dtype in self.dataclass_types:
																		lowerCamelCase_			:    Union[str, Any]   						=      {f.name for f in dataclasses.fields(lowerCAmelCase__							) if f.init}
																		lowerCamelCase_			:    List[Any]   						=      {k: v for k, v in args.items() if k in keys}
																		unused_keys.difference_update(inputs.keys()							)
																		lowerCamelCase_			:    Optional[int]   						=      dtype(**lowerCAmelCase__							)
																		outputs.append(lowerCAmelCase__							)
											if not allow_extra_keys and unused_keys:
																		raise ValueError(F"Some keys are not used by the HfArgumentParser: {sorted(lowerCAmelCase__							)}"							)
											return tuple(lowerCAmelCase__							)





				def    __SCREAMING_SNAKE_CASE     (      self							:   Optional[int]      ,							__magic_name__							:   str      ,							__magic_name__							:   bool = False							)							->			Dict:
											with open(Path(lowerCAmelCase__							)      ,							encoding="utf-8"							) as open_json_file:
																		lowerCamelCase_			:    Optional[Any]   						=      json.loads(open_json_file.read()							)
											lowerCamelCase_			:    Dict   						=      self.parse_dict(lowerCAmelCase__      ,							allow_extra_keys=lowerCAmelCase__							)
											return tuple(lowerCAmelCase__							)





				def    __SCREAMING_SNAKE_CASE     (      self							:   List[str]      ,							__magic_name__							:   str      ,							__magic_name__							:   bool = False							)							->			int:
											lowerCamelCase_			:    Optional[int]   						=      self.parse_dict(yaml.safe_load(Path(lowerCAmelCase__							).read_text()							)      ,							allow_extra_keys=lowerCAmelCase__							)
											return tuple(lowerCAmelCase__							)






'''simple docstring'''



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
lowerCAmelCase_  :							Optional[int]             =    logging.get_logger(__name__)

lowerCAmelCase_  :							List[str]             =    {
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
lowerCAmelCase_  :							Any             =    [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]
def        UpperCAmelCase (    A	:					Dict					):
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      {}
							with open(A       ,		'''r'''					) as file:
														for line_number, line in enumerate(A					):
																					SCREAMING_SNAKE_CASE       :   Dict         =      line.strip()
																					if line:
																												SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      line.split()
																												SCREAMING_SNAKE_CASE       :   Any         =      line_number
																												SCREAMING_SNAKE_CASE       :   Any         =      words[0]
																												SCREAMING_SNAKE_CASE       :   Optional[Any]         =      value
							return result
def        UpperCAmelCase (    A	:					Union[str, Any]       ,		A	:					Tuple       ,		A	:					List[Any]       ,		A	:					str       ,		A	:					int					):
							for attribute in key.split('''.'''					):
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      getattr(A       ,		A					)

							SCREAMING_SNAKE_CASE       :   Any         =      None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(A					):
																					SCREAMING_SNAKE_CASE       :   List[str]         =      PARAM_MAPPING[full_name.split('''.'''					)[-1]]
																					SCREAMING_SNAKE_CASE       :   Tuple         =      '''param'''

							if weight_type is not None and weight_type != "param":
														SCREAMING_SNAKE_CASE       :   List[Any]         =      getattr(A       ,		A					).shape
							elif weight_type is not None and weight_type == "param":
														SCREAMING_SNAKE_CASE       :   List[str]         =      hf_pointer
														for attribute in hf_param_name.split('''.'''					):
																					SCREAMING_SNAKE_CASE       :   int         =      getattr(A       ,		A					)
														SCREAMING_SNAKE_CASE       :   int         =      shape_pointer.shape

														# let's reduce dimension
														SCREAMING_SNAKE_CASE       :   Dict         =      value[0]
							else:
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      hf_pointer.shape

							if hf_shape != value.shape:
														raise ValueError(
														    F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
														    F""" {value.shape} for {full_name}"""					)

							if weight_type == "weight":
														SCREAMING_SNAKE_CASE       :   str         =      value
							elif weight_type == "weight_g":
														SCREAMING_SNAKE_CASE       :   List[str]         =      value
							elif weight_type == "weight_v":
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      value
							elif weight_type == "bias":
														SCREAMING_SNAKE_CASE       :   Dict         =      value
							elif weight_type == "param":
														for attribute in hf_param_name.split('''.'''					):
																					SCREAMING_SNAKE_CASE       :   List[str]         =      getattr(A       ,		A					)
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      value
							else:
														SCREAMING_SNAKE_CASE       :   Tuple         =      value

							logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""					)
def        UpperCAmelCase (    A	:					Optional[Any]       ,		A	:					List[str]       ,		A	:					Optional[int]       ,		A	:					Tuple       ,		A	:					Tuple					):
							SCREAMING_SNAKE_CASE       :   List[str]         =      None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(A					):
																					SCREAMING_SNAKE_CASE       :   List[str]         =      PARAM_MAPPING[full_name.split('''.'''					)[-1]]
																					SCREAMING_SNAKE_CASE       :   List[Any]         =      '''param'''

							if weight_type is not None and weight_type != "param":
														SCREAMING_SNAKE_CASE       :   str         =      '''.'''.join([key, weight_type]					)
							elif weight_type is not None and weight_type == "param":
														SCREAMING_SNAKE_CASE       :   Tuple         =      '''.'''.join([key, hf_param_name]					)
							else:
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      key

							SCREAMING_SNAKE_CASE       :   int         =      value if '''lm_head''' in full_key else value[0]


lowerCAmelCase_  :							Union[str, Any]             =    {
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}
def        UpperCAmelCase (    A	:					List[Any]       ,		A	:					Tuple       ,		A	:					Dict=None       ,		A	:					Optional[int]=None					):
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      False
							for key, mapped_key in MAPPING.items():
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      '''wav2vec2.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
														if key in name or key.split('''w2v_model.'''					)[-1] == name.split('''.'''					)[0]:
																					SCREAMING_SNAKE_CASE       :   Optional[int]         =      True
																					if "*" in mapped_key:
																												SCREAMING_SNAKE_CASE       :   int         =      name.split(A					)[0].split('''.'''					)[-2]
																												SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      mapped_key.replace('''*'''       ,		A					)
																					if "weight_g" in name:
																												SCREAMING_SNAKE_CASE       :   List[Any]         =      '''weight_g'''
																					elif "weight_v" in name:
																												SCREAMING_SNAKE_CASE       :   int         =      '''weight_v'''
																					elif "bias" in name:
																												SCREAMING_SNAKE_CASE       :   str         =      '''bias'''
																					elif "weight" in name:
																												# TODO: don't match quantizer.weight_proj
																												SCREAMING_SNAKE_CASE       :   Optional[Any]         =      '''weight'''
																					else:
																												SCREAMING_SNAKE_CASE       :   Optional[int]         =      None
																					if hf_dict is not None:
																												rename_dict(A       ,		A       ,		A       ,		A       ,		A					)
																					else:
																												set_recursively(A       ,		A       ,		A       ,		A       ,		A					)
																					return is_used
							return is_used
def        UpperCAmelCase (    A	:					Optional[int]       ,		A	:					Any       ,		A	:					int					):
							SCREAMING_SNAKE_CASE       :   List[str]         =      []
							SCREAMING_SNAKE_CASE       :   Any         =      fairseq_model.state_dict()

							SCREAMING_SNAKE_CASE       :   List[str]         =      hf_model.wavaveca.feature_extractor

							for name, value in fairseq_dict.items():
														SCREAMING_SNAKE_CASE       :   str         =      False
														if "conv_layers" in name:
																					load_conv_layer(
																					    A       ,		A       ,		A       ,		A       ,		hf_model.config.feat_extract_norm == '''group'''       ,		)
																					SCREAMING_SNAKE_CASE       :   List[str]         =      True
														else:
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      load_wavaveca_layer(A       ,		A       ,		A					)
														if not is_used:
																					unused_weights.append(A					)

							logger.warning(F"""Unused weights: {unused_weights}"""					)
def        UpperCAmelCase (    A	:					Tuple       ,		A	:					int       ,		A	:					Tuple       ,		A	:					int       ,		A	:					Union[str, Any]					):
							SCREAMING_SNAKE_CASE       :   str         =      full_name.split('''conv_layers.'''					)[-1]
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      name.split('''.'''					)
							SCREAMING_SNAKE_CASE       :   Tuple         =      int(items[0]					)
							SCREAMING_SNAKE_CASE       :   int         =      int(items[1]					)

							if type_id == 0:
														if "bias" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""					)
																					SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      value
																					logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""					)
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""					)
																					SCREAMING_SNAKE_CASE       :   str         =      value
																					logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""					)
							elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
														if "bias" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""					)
																					SCREAMING_SNAKE_CASE       :   Tuple         =      value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""					)
																					SCREAMING_SNAKE_CASE       :   Tuple         =      value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
							else:
														unused_weights.append(A					)
@torch.no_grad()
def        UpperCAmelCase (    A	:					Any       ,		A	:					Union[str, Any]       ,		A	:					Optional[Any]=None       ,		A	:					Any=None       ,		A	:					Any=True       ,		A	:					str=False					):
							if config_path is not None:
														SCREAMING_SNAKE_CASE       :   List[Any]         =      WavaVecaConfig.from_pretrained(A					)
							else:
														SCREAMING_SNAKE_CASE       :   Any         =      WavaVecaConfig()

							if is_seq_class:
														SCREAMING_SNAKE_CASE       :   List[Any]         =      read_txt_into_dict(A					)
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      idalabel
														SCREAMING_SNAKE_CASE       :   Tuple         =      WavaVecaForSequenceClassification(A					)
														SCREAMING_SNAKE_CASE       :   Tuple         =      WavaVecaFeatureExtractor(
														    feature_size=1       ,		sampling_rate=16000       ,		padding_value=0       ,		do_normalize=A       ,		return_attention_mask=A       ,		)
														feature_extractor.save_pretrained(A					)

							elif is_finetuned:
														if dict_path:
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      Dictionary.load(A					)

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					SCREAMING_SNAKE_CASE       :   List[str]         =      target_dict.pad_index
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      target_dict.bos_index
																					SCREAMING_SNAKE_CASE       :   Dict         =      target_dict.eos_index
																					SCREAMING_SNAKE_CASE       :   Dict         =      len(target_dict.symbols					)
																					SCREAMING_SNAKE_CASE       :   str         =      os.path.join(A       ,		'''vocab.json'''					)
																					if not os.path.isdir(A					):
																												logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(A					)					)
																												return
																					os.makedirs(A       ,		exist_ok=A					)
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      target_dict.indices

																					# fairseq has the <pad> and <s> switched
																					SCREAMING_SNAKE_CASE       :   str         =      0
																					SCREAMING_SNAKE_CASE       :   Tuple         =      1
																					with open(A       ,		'''w'''       ,		encoding='''utf-8'''					) as vocab_handle:
																												json.dump(A       ,		A					)
																					SCREAMING_SNAKE_CASE       :   Any         =      WavaVecaCTCTokenizer(
																					    A       ,		unk_token=target_dict.unk_word       ,		pad_token=target_dict.pad_word       ,		bos_token=target_dict.bos_word       ,		eos_token=target_dict.eos_word       ,		word_delimiter_token='''|'''       ,		do_lower_case=A       ,		)
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      True if config.feat_extract_norm == '''layer''' else False
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      WavaVecaFeatureExtractor(
																					    feature_size=1       ,		sampling_rate=16000       ,		padding_value=0       ,		do_normalize=A       ,		return_attention_mask=A       ,		)
																					SCREAMING_SNAKE_CASE       :   Any         =      WavaVecaProcessor(feature_extractor=A       ,		tokenizer=A					)
																					processor.save_pretrained(A					)

														SCREAMING_SNAKE_CASE       :   List[str]         =      WavaVecaForCTC(A					)
							else:
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      WavaVecaForPreTraining(A					)

							if is_finetuned or is_seq_class:
														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Optional[Any]         =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]       ,		arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''					)[:-1]					)}					)
							else:
														SCREAMING_SNAKE_CASE       :   str         =      argparse.Namespace(task='''audio_pretraining'''					)
														SCREAMING_SNAKE_CASE       :   Dict         =      fairseq.tasks.setup_task(A					)

														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   List[str]         =      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       ,		task=A					)

							SCREAMING_SNAKE_CASE       :   List[Any]         =      model[0].eval()

							recursively_load_weights(A       ,		A       ,		not is_finetuned					)

							hf_wavavec.save_pretrained(A					)


if __name__ == "__main__":
							lowerCAmelCase_  :							List[Any]             =    argparse.ArgumentParser()
							parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
							parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
							parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
							parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
							parser.add_argument(
							    '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
							)
							parser.add_argument(
							    '--is_seq_class',
							    action='store_true',
							    help='Whether the model to convert is a fine-tuned sequence classification model or not',
							)
							lowerCAmelCase_  :							Union[str, Any]             =    parser.parse_args()

							lowerCAmelCase_  :							Dict             =    not args.not_finetuned and not args.is_seq_class
							convert_wavaveca_checkpoint(
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.config_path,
							    args.dict_path,
							    is_finetuned,
							    args.is_seq_class,
							)




